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2025
Nuclear environments demand exceptional precision, reliability, and safety, given the high stakes involved in handling radioactive materials and maintaining reactor systems. Object-oriented assembly and disassembly operations in nuclear applications represent a cutting-edge approach to managing complex, high-stakes operations with enhanced precisio…
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UKAEA-RACE-PR(25)032024
When using deep reinforcement learning (DRL) to perform multi-robot exploration in unknown environments, the training model may produce actions that lead to unpredictable system behaviours due to the complexity and unpredictability of the surroundings. Therefore, ensuring safe exploration with DRL becomes critical. To tackle this issue, we propo…
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UKAEA-RACE-CP(23)092023
When transferring a Deep Reinforcement Learning (DRL) model from simulation to the real world, the performance could be unsatisfactory since the simulation cannot imitate the real world well in many circumstances. This results in a long period of fine-tuning in the real world. This paper proposes a self-supervised vision-based DRL method that al…
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UKAEA-RACE-CP(23)062023
Sim-and-real training is a promising alternative to sim-to-real training for robot manipulations. However, the current sim-and-real training is neither efficient, i.e., slow convergence to the optimal policy, nor effective, i.e., sizeable real-world robot data. Given limited time and hardware budgets, the performance of sim-and-real training is …
Showing 1 - 4 of 4 UKAEA Paper Results
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